Adaptive Non-local Pattern Consistency based Multi-modal Remote Sensing Image Change Detection

Han Te; Tang Yuqi; Chen Yuzeng; Zhang Fangyan; Yang Xin; Zou Bin; Feng Huihui

doi:10.11834/jrs.20233072

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Adaptive Non-local Pattern Consistency based Multi-modal Remote Sensing Image Change Detection
Pages: 1-15(2023)
Published Online： 19 December 2023 ，
DOI： 10.11834/jrs.20233072

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韩特，汤玉奇，陈玉增，张芳艳，杨欣，邹滨，冯徽徽.XXXX.基于自适应非局部模式一致性的多模态遥感影像变化检测方法.遥感学报，XX（XX）： 1-15

Han Te，Tang Yuqi，Chen Yuzeng，Zhang Fangyan，Yang Xin，Zou Bin，Feng Huihui. XXXX. Adaptive Non-local Pattern Consistency based Multi-modal Remote Sensing Image Change Detection. National Remote Sensing Bulletin， XX（XX）：1-15
韩特，汤玉奇，陈玉增，张芳艳，杨欣，邹滨，冯徽徽.XXXX.基于自适应非局部模式一致性的多模态遥感影像变化检测方法.遥感学报，XX（XX）： 1-15 DOI： 10.11834/jrs.20233072.

Han Te，Tang Yuqi，Chen Yuzeng，Zhang Fangyan，Yang Xin，Zou Bin，Feng Huihui. XXXX. Adaptive Non-local Pattern Consistency based Multi-modal Remote Sensing Image Change Detection. National Remote Sensing Bulletin， XX（XX）：1-15 DOI： 10.11834/jrs.20233072.

摘要

亚热带地区环境复杂，自然灾害频发。多模态遥感影像变化检测可在监测其环境变化与灾害现象中发挥重要作用，已成为当下遥感影像处理的研究热点。针对现有方法存在的目标空间结构特征和影像变化信息利用不足的问题，本文假设地物变化将导致对应影像区域的空间结构特征变化，提出了一种基于自适应非局部模式一致性（Adaptive Non-local Pattern Consistency， ANLPC）的多模态遥感影像变化检测方法。该方法通过度量多模态影像的空间结构变化提取影像变化信息。首先利用块相似性构建多模态影像的自适应非局部模式，实现空间结构特征表达；然后通过前/后向模式映射度量其与另一时相影像的空间结构差异；最后融合前/后向变化强度图的频率域信息获取鲁棒的变化强度图，并通过阈值分割得到二值变化检测图。本文采用4组多模态遥感影像数据集（2组光学-SAR（Synthetic Aperture Radar）数据集，2组光学-LiDAR数据集）和2组单模态遥感影像数据集（1组光学影像数据集，1组SAR影像数据集）验证了本文方法的有效性。相对于现有方法，本文方法在6组数据集中的卡帕系数KC平均至少提升17.28%。

Abstract

Object Multi-modal remote sensing image change detection is an active research area in the field of remote sensing image processing and plays a significant role in disaster monitoring

urban planning

natural resources monitoring and other domains. To address the problem of insufficient utilization of target spatial structure features and image change information in existing methods. This paper proposes that spatial structure features of unchanged regions in multi-modal images are consistent

while the spatial structure features of changed regions are different. Therefore

change information can be extracted by measuring the difference of spatial structure of multi-modal images. Thus

this paper proposes a change detection method based on adaptive non-local pattern consistency (ANLPC) for multi-modal remote sensing images.Method In this study

the basic processing unit for the images is made up of patches that overlap one another

and the target patch is defined as the construction pattern's reference patch and the other patches as homogeneous patch. The non-local mode of the image is constructed adaptively using the homogeneous patch automated selection approach

using the rank coordinate space of the target patch as the search space

in order to take into account the spatial information of the image and narrow the search area. The cross mapping of two temporal image patterns (forward and backward mapping) is achieved in this paper by adaptive nonlocal pattern mapping to precisely assess the variation between multi-modal images. Taking the forward mapping as an example

ANLPC maps the nonlocal pattern of the first temporal image into the second temporal image domain

and the difference information of the pattern in the second temporal image domain represents the change information of the multi-modal image. Similarly

it is possible to acquire the backward change information from backward mapping. The final difference map is produced by combining the forward and backward difference information based on the curvelet transform

and the binary change detection results are produced using threshold segmentation.Result Four multi-modal remote sensing image datasets (two optical-SAR (Synthetic Aperture Radar) datasets and two optical-LiDAR datasets) and two single-modal remote sensing image datasets (one optical image dataset and one SAR image dataset) are used to verify the effectiveness of this method. Compared with the existing methods

the average improvement of kappa coefficient in the six datasets is 17.28%.Conclusion To address the problem of insufficient utilization of target spatial structure features and image change information in existing methods

the adaptive nonlocal pattern is used to characterize the structural information of the image in this paper. The changed regions are measured in the same image domain by cross-mapping the nonlocal pattern to circumvent the imaging differences of multi-modal images. Meanwhile

we use difference image fusion and threshold segmentation to obtain robust change map. The proposed method shows better accuracy than the comparison methods in both single-modal and multi-modal datasets

which demonstrates its effectiveness and robustness.

关键词

多模态影像变化检测结构特征自适应非局部模式模式映射影像融合

Keywords

Multimodal imageschange detectionstructural featuresadaptive non-local patternspattern mappingimage fusion

references

Arthur D and Vassilvitskii S. 2007. k-means++: The advantages of careful seeding. Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, 1027-1035 [DOI:10.1145/1283383.1283494http://dx.doi.org/10.1145/1283383.1283494]

Gong M G, Zhang P Z, Su L Z and Liu J. 2016. Coupled dictionary learning for change detection from multisource data. IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7077-7091 [DOI: 10.1109/TGRS.2016.2594952http://dx.doi.org/10.1109/TGRS.2016.2594952]

Han T, Tang Y Q, Zou B, Feng H H and Zhang F Y. 2022. Heterogeneous images change detection method based on hierarchical extreme learning machine image transformation[J]. Journal of Geo-information Science, 24(11): 2212-2224

韩特,汤玉奇,邹滨,冯徽徽,张芳艳.2022.基于分层极限学习机影像转换的多源影像变化检测方法.地球信息科学学报, 24(11): 2212-2224[ DOI: 10.12082/dqxxkx.2022.220089]

Han, T, Tang, Y Q, Yang X, Lin Z F, Zou B and Feng H H. 2021. Change detection for heterogeneous remote sensing images with improved training of hierarchical extreme learning machine (HELM). Remote Sensing. 13:4918 [DOI:10.3390/rs13234918http://dx.doi.org/10.3390/rs13234918]

Jimenez-Sierra DA, Benítez-Restrepo HD, Vargas-Cardona HD, Chanussot J. 2020. Graph-based data fusion applied to: change detection and biomass estimation in rice crops. Remote Sensing, 12:2683 [DOI: 10.3390/rs12172683http://dx.doi.org/10.3390/rs12172683]

Krinidis S and Chatzis V. 2010. A robust fuzzy local information c-means clustering algorithm. IEEE Transactions on Image Processing,19(5):1328-1337 [DOI: 10.1109/TIP.2010.2040763http://dx.doi.org/10.1109/TIP.2010.2040763]

Li X H, Du Z S, Huang Y Y, and Tan Z Y. 2021. A deep translation (GAN) based change detection network for optical and SAR remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 179:14–34 [DOI: 10.1016/j.isprsjprs.2021.07.007http://dx.doi.org/10.1016/j.isprsjprs.2021.07.007]

Liu Z G, Li G, Mercier G, He Y and Pan Q. 2018. Change detection in heterogenous remote sensing images via homogeneous pixel transformation. IEEE Transactions on Image Processing, 27(4): 1822-1834 [DOI: 10.1109/TIP.2017.2784560http://dx.doi.org/10.1109/TIP.2017.2784560]

Luppino L T, Bianchi F M, Moser G and Anfinsen S N. 2019. Unsupervised image regression for heterogeneous change detection. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9960-9975 [DOI: 10.1109/TGRS.2019.2930348http://dx.doi.org/10.1109/TGRS.2019.2930348]

Niu X D Gong M G, Zhan T and Yang Y L. 2019. A conditional adversarial network for change detection in heterogeneous images. IEEE Geoscience and Remote Sensing Letters, 16(1): 45-49 [DOI: 10.1109/LGRS.2018.2868704http://dx.doi.org/10.1109/LGRS.2018.2868704]

Prendes J, Chabert M, Pascal F, Giros A and Tourneret J -Y. 2015. A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors. IEEE Transactions on Image Processing, 24(3):799-812 [DOI: 10.1109/TIP.2014.2387013http://dx.doi.org/10.1109/TIP.2014.2387013]

Starck J-L, Candes E J and Donoho D L. 2022. The curvelet transform for image denoising. IEEE Transactions on Image Processing, 11(6):670-684 [DOI: 10.1109/TIP.2002.1014998http://dx.doi.org/10.1109/TIP.2002.1014998]

Sui H G, Feng W Q, Li W Z, Sun K M and Xu C. 2018. Review of change detection methods for multi-temporal remote sensing imagery. Geomatics and Information Science of Wuhan University, 43(12): 1885-1898

眭海刚, 冯文卿, 李文卓, 孙开敏, 徐川. 2018. 多时相遥感影像变化检测方法综述.武汉大学学报(信息科学版), 43(12): 1885-1898 [DOI: 10.13203/j.whugis20180251http://dx.doi.org/10.13203/j.whugis20180251]

Sun Y L, Lei L, Li X, Tan X and Kuang G Y. 2021. Patch similarity graph matrix-based unsupervised remote sensing change detection with homogeneous and heterogeneous sensors. IEEE Transactions on Geoscience and Remote Sensing, 59(6): 4841-4861 [DOI:10.1109/TGRS.2020.3013673http://dx.doi.org/10.1109/TGRS.2020.3013673]

Sun Y L, Lei L, Li X. Sun H and Kuang G Y. 2021. Nonlocal patch similarity based heterogeneous remote sensing change detection. Pattern Recognition, 109:107598 [DOI:10.1016/j.patcog.2020.107598http://dx.doi.org/10.1016/j.patcog.2020.107598]

Sun Y, Lei L, Tan X, Guan D D, Wu J Z, Kuang G Y. 2022. Structured graph based image regression for unsupervised multi-modal change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 185: 16-31 [DOI:10.1016/j.isprsjprs.2022.01.004http://dx.doi.org/10.1016/j.isprsjprs.2022.01.004]

Tang Y Q, Zhang L P. 2017. Urban change analysis with multi-sensor multispectral imagery. Remote Sensing, 9(3): 252 [DOI: 10.3390/rs9030252http://dx.doi.org/10.3390/rs9030252]

Touati R and Mignotte M. 2018. An energy-based model encoding nonlocal pairwise pixel interactions for multisensor change detection. IEEE Transactions on Geoscience and Remote Sensing, 56(2):1046-1058 [DOI: 10.1109/TGRS.2017.2758359http://dx.doi.org/10.1109/TGRS.2017.2758359]

Touati R, Mignotte M and Dahmane M. 2020. Anomaly feature learning for unsupervised change detection in heterogeneous images: a deep sparse residual model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13:588-600 [DOI: 10.1109/JSTARS.2020.2964409http://dx.doi.org/10.1109/JSTARS.2020.2964409]

Touati R, Mignotte M and Dahmane M. 2020. Multi-modal change detection in remote sensing images using an unsupervised pixel pairwise-based markov random field model. IEEE Transactions on Image Processing, 29:757-767 [DPI: 10.1109/TIP.2019.2933747http://dx.doi.org/10.1109/TIP.2019.2933747]

Wan L, Zhang T and You H J. 2018. Multi-sensor remote sensing image change detection based on sorted histograms. International Journal of Remote Sensing, 39(11):753-3775 [DOI: 10.1080/01431161.2018.1448481http://dx.doi.org/10.1080/01431161.2018.1448481]

Wan Ling, Xiang Y M and You H G. 2019. A post-classification comparison method for sar and optical images change detection. IEEE Geoscience and Remote Sensing Letters, 16(7):1026-1030[DOI: 10.1109/LGRS.2019.2892432http://dx.doi.org/10.1109/LGRS.2019.2892432]

Wan Ling, Xiang Y M and You H G. 2019. An object-based hierarchical compound classification method for change detection in heterogeneous optical and SAR Images. IEEE Transactions on Geoscience and Remote Sensing, 57(12):9941-9959 [DOI: 10.1109/TGRS.2019.2930322http://dx.doi.org/10.1109/TGRS.2019.2930322]

Wu L X，Sun G Y，Miao Z L，Zhang A Z，Feng H H，Hu J，Yang Z F，Wang W，Chen B Y and Tang Y Q. 2022. On subtropical remote sensing in China： Research status， key tasks and innovative development approaches. National Remote Sensing Bulletin， 26（8）：1483-1503

吴立新，孙根云，苗则朗，张爱竹，冯徽徽，胡俊，杨泽发，王威，陈必焰，汤玉奇.2022.浅论中国亚热带遥感现状、任务与创新发展途径.遥感学报，26（8）： 1483-1503 [DOI:10.11834/jrs.20222173http://dx.doi.org/10.11834/jrs.20222173]

Wu Y, Li J H, Yuan Y Z, Qin A K, Miao Q G and Gong M G. 2022. Commonality autoencoder: learning common features for change detection from heterogeneous images. IEEE Transactions on Neural Networks and Learning Systems, 33(9):4257-427 [DOI: 10.1109/TNNLS.2021.3056238http://dx.doi.org/10.1109/TNNLS.2021.3056238]

Yang M J, Jiao L C, Liu F, Hou B, Yang S Y and Jian M. 2022. DPFL-Nets: Deep pyramid feature learning networks for multiscale change detection. IEEE Transactions on Neural Networks and Learning Systems, 33(11):6402-6416 [DOI: 10.1109/TNNLS.2021.3079627http://dx.doi.org/10.1109/TNNLS.2021.3079627]

Zhan T, Gong M G, Jiang X M and Li S W. 2018. Log-based transformation feature learning for change detection in heterogeneous images. IEEE Geoscience and Remote Sensing Letters, 15(9): 1352-1356 [DOI: 10.1109/LGRS.2018.2843385http://dx.doi.org/10.1109/LGRS.2018.2843385]

Zhan T, Gong M G, Liu J and Zhang P Z. 2018. Iterative feature mapping network for detecting multiple changes in multi-source remote sensing images. ISPRS journal of photogrammetry and remote sensing, 146:38-51 [DOI:10.1016/j.isprsjprs.2018.09.002http://dx.doi.org/10.1016/j.isprsjprs.2018.09.002]

Zhang M and Shi W Z. 2020. A feature difference convolutional neural network-based change detection method. IEEE Transactions on Geoscience and Remote Sensing, 58(10):7232-7246[DOI: 10.1109/TGRS.2020.2981051http://dx.doi.org/10.1109/TGRS.2020.2981051]

Zhao W, Wang Z R, Gong M G and Liu J. 2017. Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network. IEEE Transactions on Geoscience and Remote Sensing, 55(12): 7066-7080 [DOI: 10.1109/TGRS.2017.2739800http://dx.doi.org/10.1109/TGRS.2017.2739800]

Zhu Q Q, Guo X, Li Z Q and Li D R. 2022. A review of multi-class change detection for satellite remote sensing imagery. Geo-spatial Information Science. [DOI: 10.1080/10095020.2022.2128902http://dx.doi.org/10.1080/10095020.2022.2128902]

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